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Record W1637490460 · doi:10.3233/wor-2012-0523-2776

Linking human factors to corporate strategy with cognitive mapping techniques

2012· article· en· W1637490460 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCognitive mapCognitionProcess managementKnowledge managementStrategy mapStrategic planningComputer scienceBusinessStrategic managementPsychologyMarketing

Abstract

fetched live from OpenAlex

For human factors (HF) to avoid being considered of "side-car" status, it needs to be positioned within the organization in such a way that it affects business strategies and their implementation. Tools are needed to support this effort. This paper explores the feasibility of applying a technique from operational research called cognitive mapping to link HF to corporate strategy. Using a single case study, a cognitive map is drawn to reveal the complex relationships between human factors and achieving an organization's strategic goals. Analysis of the map for central concepts and reinforcing loops enhances understanding that can lead to discrete initiatives to facilitate integration of HF. It is recommended that this technique be used with senior managers to understand the organizations` strategic goals and enhance understanding of the potential for HF to contribute to the strategic goals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.287
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it